Skip to main content
Glama
metricool

mcp-metricool

Official
by metricool

get_twitch_videos

Retrieve Twitch videos from your Metricool account by specifying a date range and blog ID. Simplify data extraction for analysis or reporting.

Instructions

Get the list of Twitch Videos from your Metricool account.

Args: init date: Init date of the period to get the data. The format is YYYYMMDD end date: End date of the period to get the data. The format is YYYYMMDD blog id: Blog id of the Metricool brand account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
blog_idYes
end_dateYes
init_dateYes

Implementation Reference

  • Handler function decorated with @mcp.tool() which registers it as an MCP tool. Implements logic to fetch Twitch videos data from Metricool API via HTTP GET request.
    @mcp.tool()
    async def get_twitch_videos(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Twitch Videos from your Metricool account.
    
        Args:
         init date: Init date of the period to get the data. The format is YYYYMMDD
         end date: End date of the period to get the data. The format is YYYYMMDD
         blog id: Blog id of the Metricool brand account.
        """
    
        url = f"{METRICOOL_BASE_URL}/stats/twitch/videos?start={init_date}&end={end_date}&blogId={blog_id}&userId={METRICOOL_USER_ID}&integrationSource=MCP"
    
        response = await make_get_request(url)
    
        if not response:
            return ("Failed to get Twitch Videos")
    
        return response
  • The @mcp.tool() decorator registers the get_twitch_videos function as an MCP tool in the FastMCP server.
    @mcp.tool()
    async def get_twitch_videos(init_date: str, end_date: str, blog_id: int) -> str | dict[str, Any]:
        """
        Get the list of Twitch Videos from your Metricool account.
    
        Args:
         init date: Init date of the period to get the data. The format is YYYYMMDD
         end date: End date of the period to get the data. The format is YYYYMMDD
         blog id: Blog id of the Metricool brand account.
        """
    
        url = f"{METRICOOL_BASE_URL}/stats/twitch/videos?start={init_date}&end={end_date}&blogId={blog_id}&userId={METRICOOL_USER_ID}&integrationSource=MCP"
    
        response = await make_get_request(url)
    
        if not response:
            return ("Failed to get Twitch Videos")
    
        return response
  • Docstring provides input schema description for the tool parameters: init_date (YYYYMMDD), end_date (YYYYMMDD), blog_id (int). Return type: str | dict[str, Any].
    """
    Get the list of Twitch Videos from your Metricool account.
    
    Args:
     init date: Init date of the period to get the data. The format is YYYYMMDD
     end date: End date of the period to get the data. The format is YYYYMMDD
     blog id: Blog id of the Metricool brand account.
    """
  • Imports make_get_request utility used to perform the API request in the handler.
    from mcp_metricool.utils.utils import make_get_request
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving data from 'your Metricool account,' implying a read-only operation, but doesn't clarify authentication needs, rate limits, pagination, error conditions, or what the returned list includes (e.g., video metadata, performance metrics). For a data-fetching tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the purpose, followed by a structured 'Args:' section with parameter details. There's no redundant information. It could be slightly more concise by integrating the parameter explanations into a single paragraph, but overall, it's efficient and well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and parameters but lacks behavioral details (e.g., response format, error handling) and usage context. Without annotations or an output schema, the agent must infer these aspects, making the description incomplete for reliable tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides clear semantics for all three parameters: 'init date' and 'end date' define the period with format YYYYMMDD, and 'blog id' specifies the Metricool brand account. This adds meaningful context beyond the schema's basic types (string/integer). However, it doesn't explain where to find the 'blog id' or validate date ranges, leaving minor gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get the list of Twitch Videos from your Metricool account.' It specifies the verb ('Get') and resource ('Twitch Videos'), and distinguishes it from siblings by focusing on Twitch videos rather than other platforms like Facebook or Instagram. However, it doesn't explicitly differentiate from similar tools like 'get_tiktok_videos' or 'get_youtube_videos' beyond the platform name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a Metricool account with Twitch integration), compare it to sibling tools (e.g., 'get_tiktok_videos' for TikTok content), or specify use cases (e.g., for analytics vs. scheduling). The only implicit context is the date range requirement, but this is parameter-specific rather than usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/metricool/mcp-metricool'

If you have feedback or need assistance with the MCP directory API, please join our Discord server